如何解决如何纠正时间序列数据上的不匹配映射
生成数据的步骤：
import pandas as pd
a = [2,3,4,5,6,8,7,1,2,6]
idx = pd.date_range("2018-01-01",periods=len(a),freq="H")
ts = pd.Series(a,index=idx)
我想应用一个简单的函数来映射基于一些初始和评估条件参数的时间序列的值：
state = False
s = 'outside market'
def check_market_state(x):
global state
global s
if state == False and x < 5 or x == 0:
s = 'outside market'
state = False
if state == False and x >= 5:
s = 'entered market'
state = True
if state == True and x !=0 and x >= 5:
s = 'inside market'
if state == True and x >=5:
s = 'inside market'
if state == True and x == 0 :
s = 'exit market'
state = False
return s
条件：
如果阈值为 5 ：
外部市场如果 x 小于 5 并且我们之前是外部市场
如果 x 大于等于 5 则进入市场并且我们之前在市场之外
如果 x 小于 5 或大于 5 但不等于 0 并且我们之前进入市场或内部市场
如果 x 等于 0 退出市场
期望输出：
解决方法
假设您有这个数据框（ts 和 idx 取自您的问题）：
df = pd.DataFrame({"Percent_change": ts},index=idx)
print(df)
Percent_change
2018-01-01 00:00:00 2
2018-01-01 01:00:00 3
2018-01-01 02:00:00 4
2018-01-01 03:00:00 5
2018-01-01 04:00:00 6
2018-01-01 05:00:00 0
2018-01-01 06:00:00 8
2018-01-01 07:00:00 7
2018-01-01 08:00:00 1
2018-01-01 09:00:00 3
2018-01-01 10:00:00 4
2018-01-01 11:00:00 0
2018-01-01 12:00:00 6
2018-01-01 13:00:00 4
2018-01-01 14:00:00 0
2018-01-01 15:00:00 2
2018-01-01 16:00:00 4
2018-01-01 17:00:00 0
2018-01-01 18:00:00 4
2018-01-01 19:00:00 5
2018-01-01 20:00:00 0
2018-01-01 21:00:00 1
2018-01-01 22:00:00 7
2018-01-01 23:00:00 0
2018-01-02 00:00:00 1
2018-01-02 01:00:00 8
2018-01-02 02:00:00 5
2018-01-02 03:00:00 3
2018-01-02 04:00:00 6
那么：
def signal():
current_state = "Outside market"
while True:
pct_change = yield current_state
if (
current_state in ("Outside market","Market exit")
and pct_change >= 5
):
current_state = "Entered market"
elif current_state == "Entered market" and pct_change > 0:
current_state = "Inside market"
elif current_state is "Market exit" and pct_change < 5:
current_state = "Outside market"
elif (
current_state in ("Entered market","Inside market")
and pct_change == 0
):
current_state = "Market exit"
s = signal()
next(s)
df["Signal"] = df["Percent_change"].apply(lambda x: s.send(x))
df["Timestamp"] = pd.to_datetime(
np.where(
((df["Signal"] == "Entered market") | (df["Signal"] == "Market exit")),df.index,pd.NaT,)
)
print(df)
Percent_change Signal Timestamp
2018-01-01 00:00:00 2 Outside market NaT
2018-01-01 01:00:00 3 Outside market NaT
2018-01-01 02:00:00 4 Outside market NaT
2018-01-01 03:00:00 5 Entered market 2018-01-01 03:00:00
2018-01-01 04:00:00 6 Inside market NaT
2018-01-01 05:00:00 0 Market exit 2018-01-01 05:00:00
2018-01-01 06:00:00 8 Entered market 2018-01-01 06:00:00
2018-01-01 07:00:00 7 Inside market NaT
2018-01-01 08:00:00 1 Inside market NaT
2018-01-01 09:00:00 3 Inside market NaT
2018-01-01 10:00:00 4 Inside market NaT
2018-01-01 11:00:00 0 Market exit 2018-01-01 11:00:00
2018-01-01 12:00:00 6 Entered market 2018-01-01 12:00:00
2018-01-01 13:00:00 4 Inside market NaT
2018-01-01 14:00:00 0 Market exit 2018-01-01 14:00:00
2018-01-01 15:00:00 2 Outside market NaT
2018-01-01 16:00:00 4 Outside market NaT
2018-01-01 17:00:00 0 Outside market NaT
2018-01-01 18:00:00 4 Outside market NaT
2018-01-01 19:00:00 5 Entered market 2018-01-01 19:00:00
2018-01-01 20:00:00 0 Market exit 2018-01-01 20:00:00
2018-01-01 21:00:00 1 Outside market NaT
2018-01-01 22:00:00 7 Entered market 2018-01-01 22:00:00
2018-01-01 23:00:00 0 Market exit 2018-01-01 23:00:00
2018-01-02 00:00:00 1 Outside market NaT
2018-01-02 01:00:00 8 Entered market 2018-01-02 01:00:00
2018-01-02 02:00:00 5 Inside market NaT
2018-01-02 03:00:00 3 Inside market NaT
2018-01-02 04:00:00 6 Inside market NaT
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